Information & Management最新文献

The current understanding of how digital orientation and corporate social responsibility activities collectively affect alliance relationship stability remains limited. This study explores the impact of these two strategic orientations on the alliance relationship stability in contract farming. Using a sample of Chinese agribusinesses, the results show that the two strategic orientations improve relationship quality between agribusinesses and farmers, which in turn enhances alliance relationship stability. Relationship quality fully mediates the effect of digital orientation on alliance relationship stability and partially mediates the effect of corporate social responsibility activities. The balance between the two strategic orientations is critical for alliance relationship stability.

Online games have emerged as a breath of fresh air in the lives of the current digital generation amidst the backdrop of COVID-19 and subsequent global lockdowns. Within Internet communities, online gamers actively engage in knowledge sharing, exchanging insights and updates on their experiences. This study introduces a model designed for Massive Multiplayer Online Gamers of Travian, focusing on intrinsic factors such as perceived usefulness, perceived ease of use, and the flow state, supported by the Technology Acceptance Model. Amidst the COVID-19 pandemic, with lockdowns and quarantine as moderators, this study uses partial least squares structural equation modeling (PLS-SEM) methodology, encompassing 496 gamers, to elucidate the impact of technology acceptance and flow theory on intrinsic motivational factors that drive online gamers' knowledge-sharing activities, underpinning the social protection theory. This pioneering research explores the implications of COVID-19 restrictions on gamers' behavioral patterns, contributing significantly to the existing literature by providing a deeper psychological understanding of the less-explored realm of diverse online gamers' activities during the pandemic.

Firm-hosted open innovation platforms (OIPs) enable multidirectional communication and interaction among users, and they have become a vital IT artifact that absorbs external resources for enterprise innovation. A key challenge for OIPs is how to motivate users’ idea contributions. The support community appears as a Q&A section in a few OIPs, in which the special role of user behavior on individuals’ idea contribution is understudied. Based on a theoretical synthesis of innovation management studies, we propose hypotheses regarding the spillover effects of user interactions in a support community (i.e., help giving and help seeking) on individuals’ innovation performance (i.e., the quantity and quality of ideas). Using a dataset from a typical OIP, we employ two empirical studies to investigate the spillover effects of user interactions and their characteristics, including breadth and depth, on innovation performance. Our results confirm the existence of spillover effects and reveal a positive effect of interaction breadth and a nonlinear effect of interaction depth on innovation. Notably, the nonlinear relationship can be moderated by the interaction breadth. Our findings provide both theoretical and practical implications for user innovation and OIP operations.

Although the extant literature demonstrates that mobile device icons change consumers’ cognition of review helpfulness, it reports contradictory findings: displaying mobile device icons either helps or hurts review helpfulness. Drawing on the instability of peripheral routes (ELM) and perceived effort, we found that the impact of mobile device icons on review helpfulness is contingent on review writing effort, represented by review length. Econometric and experimental analyses ensured the external and internal validity of results, respectively. Moreover, we applied interpretable machine learning to designing mobile device icons varying with review writing effort, aiming at maximizing their effects on review helpfulness.

We propose a personalized algorithmic decision support (PADS) tool, facilitating premium pricing for pregnant women by accounting for the risk of gestational diabetes mellitus (GDM). The insurance premium with PADS is derived from true negative and positive ratios of machine learning algorithms. Hybrid sampling with uniform designs improves ML algorithm performance under unbalanced data. Feature selection approaches guarantee the accuracy and interpretability of the prediction models. PADS reduces the premium for most patients with a lower risk of GDM. A smaller fraction of patients will pay more premiums under PADS; however, they can benefit from an earlier GDM diagnosis.

One major challenge physicians face in online health communities (OHCs) is how to better support patients in online consultations. Addressing this issue requires an in-depth understanding of the differences and relationships between different types of professional support provided by physicians. We use text mining to quantify emotional and informational support in physician replies and compare their impacts on physician returns in OHCs. Our analysis suggests that informational support has a larger and longer-lasting effect than emotional support, and a complementary relationship exists between these two support types. We also find that jargon usage negatively moderates the effect of informational support.

Building on social learning theory, we developed a model to examine how observational learning and reinforcement learning collectively determine online impulsive buying in social commerce. We empirically validated that both social learning processes can ultimately formulate impulsive buying with a mixed-methods approach, including a quantitative field survey and a qualitative semistructured interview in the same research inquiry. Our study contributes to the literature by offering a social learning perspective of online impulsive buying in social commerce. It provides practical guidance toward leveraging social learning strategies for promoting social commerce.

This study examines the impact of live chat on purchase decisions in the context of online outsourcing platforms. A theoretical research model based on signaling theory is proposed. The results of an econometric model based on a unique dataset from a large online outsourcing platform demonstrate that both affective signals (reflected by politeness and sentiment valence) and informative signals (reflected by information quantity and response timeliness) positively affect purchase decisions. In addition, these effects are found to be significantly influenced by consumers’ prior experience. Specifically, the findings show that consumers’ prior experience undermines the effect of politeness and sentiment valence on purchase decisions, but strengthens the effect of information quantity. In general, this study not only offers a comprehensive understanding of live chat in an outsourcing platform, but also yields insights into how live chat tools can be designed and used to promote online transactions.

We investigate an intriguing strategy where rival content platforms jointly launch a membership bundle that allows consumers to access both of their platforms at a reduced price. We highlight that this approach transforms the platform relationship from pure competition to coopetition. Using a game-theoretical model, we show how a membership bundle significantly influences additional content distribution and acquisition outcomes and the associated welfare implications. We further undertake various extensions to demonstrate the robustness of our findings. We enrich the understanding of platform coopetition and derive practical implications with relevance for both platforms and content providers.

Different uses (e.g., routine use and innovative use) of mHealth technology are important for users to synergistically utilize rich health resources. Capturing the characteristics of postadoption usage behavior (i.e., various and free) and the research gaps in previous literature, this study theorizes a research model of different uses of fitness apps, drawing on the exploitation–exploration framework and goal-setting theory. The results indicate that both health goals and eHealth literacy positively influence routine use and innovative use of fitness apps to achieve self-management, and environmental factors andersonal characteristics moderate the relationships in different uses, respectively, thus refining theoretical boundaries.